Systems and methods of database entity creation

Abstract

Systems and methods are provided for retrieving metadata of one or more existing database entities. Vector embeddings for the one or more existing database entities may be generated. Data model attributes for a new database entity may be received, and new input metadata may be generated based on the data model attributes. A new vector embedding may be based on the generated new input metadata. One or more similarities may be determined between the generated new vector embedding and at least one of the generated vector embeddings of the one or more existing database entities. A recommendation of whether at least one of the generated vector embeddings that is determined to be similar to the new vector embedding is to be added to or modified based on the generated new input metadata may be received.

1 . A method comprising:
 retrieving, at a server, metadata of one or more existing database entities;   generating vector embeddings for the one or more existing database entities;   receiving, at the server, data model attributes for a new database entity and generating new input metadata based on the data model attributes;   generating, at the server, a new vector embedding based on the generated new input metadata;   determining, at the server, one or more similarities between the generated new vector embedding and at least one of the generated vector embeddings of the one or more existing database entities; and   receiving, at the server, a recommendation of whether at least one of the generated vector embeddings that is determined to be similar to the new vector embedding is to be added to or modified based on the generated new input metadata.   
     
     
         2 . The method of  claim 1 , further comprising:
 denormalizing the retrieved metadata of the one or more existing database entities to extract one or more parameters.   
     
     
         3 . The method of  claim 1 , further comprising:
 storing the generated vector embeddings in a storage device that is communicatively coupled to the server.   
     
     
         4 . The method of  claim 1 , further comprising:
 embedding, at the server, the data model attributes into a prompt; and   transmitting, at the server, the prompt to a generative artificial intelligence system that is communicatively coupled to the server.   
     
     
         5 . The method of  claim 4 , wherein the data model attributes embedded in the prompt include instructions for generating the new input metadata. 
     
     
         6 . The method of  claim 1 , wherein the determining the one or more similarities comprises:
 determining a highest similarity score between the generated new vector embedding and at least one of the generated vector embeddings.   
     
     
         7 . The method of  claim 6 , further comprising:
 determining whether the highest similarity score is greater than or equal to a predetermined threshold score for similarity.   
     
     
         8 . The method of  claim 1 , further comprising:
 generating, at a generative artificial intelligence system communicatively coupled to the server, the recommendation for adding to or modifying the at least one of the generated vector embeddings that is determined to be similar; and   transmitting the generated recommendation to the server.   
     
     
         9 . The method of  claim 1 , further comprising:
 generating, at a generative artificial intelligence system communicatively coupled to the server, the recommendation for leaving unchanged the at least one of the generated vector embeddings that is determined to be similar; and   transmitting the generated recommendation to the server.   
     
     
         10 . A system comprising:
 a server communicatively coupled to a database system, the server configured to:
 retrieve metadata of one or more existing database entities of the database system; 
 generate vector embeddings for the one or more existing database entities; 
 receive data model attributes for a new database entity and generate new input metadata based on the data model attributes; 
 generate a new vector embedding based on the generated new input metadata; 
 determine one or more similarities between the generated new vector embedding and at least one of the generated vector embeddings of the one or more existing database entities; and 
   receiving, at the server, a recommendation of whether at least one of the generated vector embeddings that is determined to be similar to the new vector embedding is to be added to or modified based on the generated new input metadata.   
     
     
         11 . The system of  claim 10 , wherein the server is configured to denormalize the retrieved metadata of the one or more existing database entities to extract one or more parameters. 
     
     
         12 . The system of  claim 10 , further comprising:
 a storage device communicatively coupled to the server,   wherein the server is configured to store the generated vector embeddings in the storage device.   
     
     
         13 . The system of  claim 10 , wherein the server is configured to embed the data model attributes into a prompt, and transmit the prompt to a generative artificial intelligence system that is communicatively coupled to the server. 
     
     
         14 . The system of  claim 13 , wherein the data model attributes embedded in the prompt include instructions for generating the new input metadata. 
     
     
         15 . The system of  claim 10 , wherein the server is configured to determine the one or more similarities by determining a highest similarity score between the generated new vector embedding and at least one of the generated vector embeddings. 
     
     
         16 . The system of  claim 15 , wherein the server is configured to determine whether the highest similarity score is greater than or equal to a predetermined threshold score for similarity. 
     
     
         17 . The system of  claim 10 , further comprising:
 a generative artificial intelligence system communicatively coupled to the server,   wherein the generative artificial intelligence system is configured to generate the recommendation for adding to or modifying the at least one of the generated vector embeddings that is determined to be similar and transmit the generated recommendation to the server.   
     
     
         18 . The system of  claim 10 , further comprising:
 a generative artificial intelligence system communicatively coupled to the server,   wherein the generative artificial intelligence system is configured to generate the recommendation for leaving unchanged the at least one of the generated vector embeddings that is determined to be similar and transmit the generated recommendation to the server.